def test_predict_df_multitask(self): c = ChildClassifier(features=["x", "y"], targets=["c", "d"]) res = c.predict(DATA_FRAME) assert is_frame(res) assert np.array_equal(res.columns, ["c", "d"]) assert np.array_equal(res.index, DATA_FRAME.index)
def test_predict_df_multitask(self): c = ChildRegressor(features=['x', 'y'], targets=['c', 'd']) res = c.predict(DATA_FRAME) assert is_frame(res) assert np.array_equal(res.columns, ['c', 'd']) assert np.array_equal(res.index, DATA_FRAME.index)
def test_predict_log_proba_df(self): c = ChildClassifier(features=["x", "y"], targets=["c"]) res = c.predict_log_proba(DATA_FRAME) assert is_frame(res) assert np.array_equal(res.index, DATA_FRAME.index) assert isinstance(res.columns, pd.MultiIndex) assert np.array_equal(res.columns.get_level_values(0), ["c", "c"]) assert np.array_equal(res.columns.get_level_values(1), [0, 1])
def test_predict_log_proba_arr_pdmode(self): c = ChildClassifier(features=["x", "y"], targets=["c"]) with pytest.warns(CompatabilityWarning): res = c.predict_log_proba(TWO_D_ARRAY) assert is_frame(res) assert np.array_equal(res.index, range(len(TWO_D_ARRAY))) assert isinstance(res.columns, pd.MultiIndex) assert np.array_equal(res.columns.get_level_values(0), ["c", "c"]) assert np.array_equal(res.columns.get_level_values(1), [0, 1])
def test_transform_df(self): res = ChildTransformer(features=['x', 'y']).transform(DATA_FRAME) assert is_frame(res) assert np.array_equal(res.columns, ['z', 'y']) assert np.array_equal(DATA_FRAME.index, res.index)
def test_transform_arr_pdmode(self): with pytest.warns(CompatabilityWarning): res = ChildTransformer(features=['x', 'y']).transform(TWO_D_ARRAY) assert is_frame(res) assert np.array_equal(res.columns, ['z', 'y'])
def test_removes_novariance_column(): v_t = VarianceThreshold(0) res_df = v_t.fit_transform(DATA) assert is_frame(res_df) assert "zero_var" not in res_df.columns